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5 Conclusion

This paper investigated the problem of predicting which author will adopt the novel topics, and tested two algorithms using the DBLP dataset. To solve this problem, the basis is to find the inherent influence network from past observations. The first approach NetInf* is based on the direct observations of past following behavior on topic adoption. It has the shortcoming that the prediction performance is much dependent on the amount of collected past cascades and whether the novel topic cascade is relevant to these past

Fig. 4. Feature Weight Distribution

Fig. 5. Feature Weight Distribution

cascades. Though all topic terms selected are in the same area (machine learning), all these terms are unique. Each topic can have its own unique cascade which could be totally different from past ones. Alternatively, HetNetInf defines the author's following behavior based on the factors affecting the adoption process, such as social connections and topic popularity. Given the adoption information of the author's social connection peers and the topic popularity, it can estimate the probability the author adopting the topic, even the topic is totally new and irrelevant to past ones. The experiment using DBLP shows that HetNetInf outperforms NetInf* in predicting the novel topic adoption. Furthermore, HetNetInf provides information on individual author's preference on the influence factors.


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